Online Classification

Online classification focuses on building models that can learn and adapt to incoming data streams in real-time, aiming to minimize prediction errors and computational costs. Current research emphasizes efficient algorithms for handling imbalanced data, concept drift (changing data distributions), and high-dimensional data, often employing ensemble methods like online bagging and boosting, or adapting existing models like Support Vector Machines and Kalman filters for online use. These advancements are crucial for applications requiring immediate decision-making from continuous data flows, such as real-time video analysis, fraud detection, and personalized education. The development of robust and efficient online classifiers is driving progress in both theoretical understanding of online learning and practical deployment in various domains.